YouTube Transcript Summarizer
M Sharath Chandra
Department of Computer Science and Engineering Institute of Aeronautical Engineering
Hyderabad, India 22951a05j2@iare.ac.in
K Shanmukh Preetham
Department of Computer Science and Engineering Institute of Aeronautical Engineering
Hyderabad, India 22951a05j1@iare.ac.in
R Vishnu
Department of Computer Science and Engineering Institute of Aeronautical Engineering
Hyderabad, India 22951a05r4@iare.ac.in
Mr. M Hari Krishna
Department of Computer Science and Engineering Institute of Aeronautical Engineering
Hyderabad, India m.harikrishna@iare.ac.in
Abstract—The volume of online video content is increasing rapidly, which creates a strong need for more effective ways to locate and utilize valuable information. YouTube is one of the largest video platforms, hosting a vast number of videos that contain useful knowledge. However, watching all these videos manually is time-consuming and not very efficient. This paper presents an AI-driven tool that automatically extracts text from YouTube videos and generates concise, clear summaries using advanced Natural Language Processing (NLP) techniques.
The system uses methods to extract text from videos and then applies transformer models such as BART and T5 to create summaries. It cleans up the text to eliminate unnecessary parts, divides long texts into smaller segments for more effective processing, and produces summaries that retain the main ideas while being significantly shorter.
The results demonstrate that this system significantly reduces video content while preserving the key meaning, making infor- mation more accessible and easier to use. The solution is capable of handling large volumes of content and can be expanded to support multiple languages, real-time processing, and integration with various tools like educational applications and content recommendation systems. This work contributes to the field of automated text summarization by providing a practical solution that links video content with NLP technologies.
Index Terms—Transcript Summarization, Natural Language Processing, Abstractive Summarization, Transformer Models, BART, T5, Text Processing, Machine Learning, Video Content Analysis
